As referenced in my last blog post, useR Delhi NCR is all set to host our second meetup on 15th December, i.e. upcoming Saturday. We’ve finalized two exciting speaker sessions for the same. They’re as follows: Basics of Shiny and … Continue reading R community update: announcing sessions for useR Delhi December meetup
Time really does fly. It’s been 5 months since Delhi NCR useR group had come into being and our first meetup. It was a successful event which included sessions featuring an R-core member and a veteran data scientist. More importantly, … Continue reading R community update: announcing useR Delhi December meetup and CFP
In my last post, I’d pointed out the importance of a local community and described how Delhi NCR useR group came about. It’s been a month since we’d started the group and I’m excited to announce that our first meetup is scheduled to take place on 14th July 2018, i.e. upcoming Saturday. Venue for the event is SocialCops headquarters in Saket, New Delhi. As a budding community, support from established organizations is a big boost, and we’re grateful to SocialCops for coming onboard as mentors and venue partners. Event details can be found on meetup and you can RSVP here … Continue reading R community update: announcing useR Delhi July meetup
“The way to change the world is through individual responsibility and taking local action in your own community.” – Jeff Bridges Over the past few years, R’s adoption has grown rapidly throughout the world. Much of it can be attributed to the growth of ‘data science’ as a domain. But R’s popularity primarily exists because of its amazing community and their contributions. Be it through open source development, Twitter (#rstats), discussion forums or programs such as Google Summer of Code (GSoC), there are numerous channels for beginners and practitioners to interact with each other. I’ve had the opportunity to interact … Continue reading R community update: announcing New Delhi useR meetup group
Right from my undergrad days when I was starting out with machine learning to this date, my admiration for Kaggle continues to grow. In addition to being synonymous with and popularizing data science competitions, the platform has served as a launching pad and breeding ground for countless data science and machine learning practitioners around the world, including yours truly. In fact, skills I’d picked up from the platform are part of the reason that I recently got to join SocialCops, a company I’d admired for years. However, I hadn’t been on the platform in 2017 as much as I would … Continue reading Kaggle Learn review: there is a deep learning track and it is worth your time
“Study hard what interests you the most in the most undisciplined, irreverent and original manner possible” – Richard Feynman As a deeply confused and somewhat optimistic sophomore, I was in a habit of taking witty quotes more seriously than most. The one above, for example, has guided how I have went about studying Machine Learning and related topics for the last two years or so. Then again, as a chemical engineering major in an Indian college with a tragically rigid curriculum, I didn’t have much of a choice. Fast forward a couple of years and after a few online courses, … Continue reading How the ‘why’s drove the ‘what’: Epilogue
Always code as if the guy who ends up maintaining your code will be a violent psychopath who knows where you live. – Martin Golding In previous posts, I had discussed how Rperform can be used to obtain and visualize package performance data. However, real-world software development is a collaborative process. Thus, automating performance testing for your package is not only a good idea, it’s a critical one; testing projects locally might not be good enough. This post will cover usage of Rperform with Travis CI for automated performance testing. More importantly, we will be able to assess performance impact … Continue reading Analyze pull requests and Travis builds using Rperform
I recently participated in a weekend-long data science hackathon, titled ‘The Smart Recruits’. Organized by the amazing folks at Analytics Vidhya, it saw some serious competition. Although my performance can be classified as decent at best (47 out of 379 participants), it was among the more satisfying ones I have participated in on both AV (profile) and Kaggle (profile) over the last few months. Thus, I decided it might be worthwhile to try and share some insights as a data science autodidact. The problem The competition required us to use historical data to create a model to help an organization … Continue reading Data Science Competitions 101: Anatomy and Approach
“In God we trust. All others must bring data.” – W. Edwards Deming In a previous post, I had discussed how Rperform uses the grammar of graphics approach to visualize an R package’s performance in terms of runtime and memory usage. The visualizations contribute significantly towards Rperform’s mission to allow package developers to quantify, analyze and visualize performance. However, at times you, the developer, might want to play with the data instead to perform analysis of your own. After going through this post, that is exactly what you would be able to do. Background If you are new to Rperform, consider … Continue reading Obtaining package performance data using Rperform
tl;dr Code for scraping GSoC data from Google Developers website for the years 2005-08 and storing it as csv files can be found here: GSoC data digging on Github Two csv files will be created: org_numbers.csv (containing information about the … Continue reading Digging into GSoC Data: Scraping 101 (Part 1)
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